
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
import warnings
warnings.filterwarnings("ignore")


df = pd.read_csv('netflix_titles.csv')


df_original = df.copy()


print("dataframe shape :", df.shape)
df.head()

dataframe shape : (7787, 12)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7787 entries, 0 to 7786
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   show_id       7787 non-null   object
 1   type          7787 non-null   object
 2   title         7787 non-null   object
 3   director      5398 non-null   object
 4   cast          7069 non-null   object
 5   country       7280 non-null   object
 6   date_added    7777 non-null   object
 7   release_year  7787 non-null   int64 
 8   rating        7780 non-null   object
 9   duration      7787 non-null   object
 10  listed_in     7787 non-null   object
 11  description   7787 non-null   object
dtypes: int64(1), object(11)
memory usage: 730.2+ KB


df['type'].value_counts().index

Index(['Movie', 'TV Show'], dtype='object')


fig = px.pie(df,values=df['type'].value_counts().values,names=df['type'].value_counts().index,
             title='Movie vs. TV Show on Netflix',
             color_discrete_sequence= px.colors.qualitative.Bold)

fig.update_traces(textposition='inside', textinfo='percent+label',
                 marker_line_width=3, marker_line_color='white' )
fig.show()


df_country = df.copy()
df_country = df.groupby(['country','type']).count()['show_id'].reset_index()
df_country.rename(columns={'show_id':'count'},inplace=True)

# transform the country col with only 1 country in each row
#df_country = pd.concat([pd.Series(row['count'],row['country'].split(', ')) for _,row in df_country.iterrows()]).reset_index()
df_country = pd.concat([pd.DataFrame([[row['type'],row['count']]],row['country'].split(', ')) for _,row in df_country.iterrows()]).reset_index()
df_country.rename(columns={'index':'country',0:'type',1:'count'},inplace=True)
df_country = df_country.groupby(['country','type']).sum()['count'].reset_index()
df_country.head(1)


# build the top 20 list

df_country_20 = df_country.groupby(['country']).sum().sort_values(by='count',ascending=False).head(20)
top_20_lst = list(df_country_20.index)
#top_20_lst


# shortlist the dataframe to match the top 20 list
top_20_df = df_country[df_country['country'].apply(lambda x: x in top_20_lst)]

# plot
fig = px.bar(top_20_df,'country','count',color='type',title='Top 20 countries with most content',height=500,
            category_orders = {'country':top_20_lst},
            color_discrete_sequence= px.colors.qualitative.Bold)

fig.show()


def target(rating):
    
    #4 categories
    target_lst = {
    'TV-PG': 'Older Kids',
    'TV-MA': 'Adults',
    'TV-Y7-FV': 'Older Kids',
    'TV-Y7': 'Older Kids',
    'TV-14': 'Teens',
    'R': 'Adults',
    'TV-Y': 'Kids',
    'NR': 'Adults',
    'PG-13': 'Teens',
    'TV-G': 'Kids',
    'PG': 'Older Kids',
    'G': 'Kids',
    'UR': 'Adults',
    'NC-17': 'Adults'
    }
    
    return target_lst[rating]


# prepare the dataframe for the plot

df_rating = df.copy()

#remove null rating values - 7 rows
df_rating.rating.isna().sum()
df_rating.dropna(subset=['rating'],inplace = True)

#create the target column
df_rating['target'] = df_rating['rating'].apply(target)
#df_rating.head(1)

# Prepare the country-rating df + trnasform the country col with only 1 country in each row
df_rating = df_rating.groupby(['country','target']).count()['show_id'].reset_index()
df_rating.rename(columns={'show_id':'count'},inplace=True)

df_rating = pd.concat([pd.DataFrame([[row['target'],row['count']]],row['country'].split(', ')) for _,row in df_rating.iterrows()]).reset_index()
df_rating.rename(columns={'index':'country',0:'target',1:'count'},inplace=True)
df_rating = df_rating.groupby(['country','target']).sum()['count'].reset_index()
df_rating.head(1)


# prepare top 10 list (by cropping the top 20 list built previously)
top_10_lst = top_20_lst[0:10]

# filter the dataframe to match the top 10 list
top_10_df = df_rating[df_rating['country'].apply(lambda x: x in top_10_lst)]

#pivot country x target
top_10_df = top_10_df.pivot_table(index='country',columns='target',values='count')

#normalize the values
top_10_df['total'] = top_10_df.sum(axis=1)
top_10_df = top_10_df.sort_values(by='total')
top_10_df = round(top_10_df.div(top_10_df['total'],axis=0)*100,1)
top_10_df_x = top_10_df.iloc[:,0:4].values
top_10_df_y = top_10_df.index


# prepare the plot

top_labels = ['Adults', 'Teens', 'Older Kids', 'Kids']

colors = ['rgba(38, 24, 74, 0.8)', 'rgba(71, 58, 131, 0.8)',
          'rgba(122, 120, 168, 0.8)', 'rgba(164, 163, 204, 0.85)'
         ]

x_data = top_10_df_x

y_data = top_10_df_y

fig = go.Figure()

for i in range(0, len(x_data[0])):
    for xd, yd in zip(x_data, y_data):
        fig.add_trace(go.Bar(
            x=[xd[i]], y=[yd],
            orientation='h',
            marker=dict(
                color=colors[i],
                line=dict(color='rgb(248, 248, 249)', width=1)
            )
        ))

fig.update_layout(
    xaxis=dict(
        showgrid=False,
        showline=False,
        showticklabels=False,
        zeroline=False,
        domain=[0.15, 1]
    ),
    yaxis=dict(
        showgrid=False,
        showline=False,
        showticklabels=False,
        zeroline=False,
    ),
    barmode='stack',
    paper_bgcolor='rgb(248, 248, 255)',
    plot_bgcolor='rgb(248, 248, 255)',
    margin=dict(l=120, r=10, t=140, b=80),
    showlegend=False,
    title='Target audience in the top 10 countries'
)

annotations = []

for yd, xd in zip(y_data, x_data):
    # labeling the y-axis
    annotations.append(dict(xref='paper', yref='y',
                            x=0.14, y=yd,
                            xanchor='right',
                            text=str(yd),
                            font=dict(family='Arial', size=14,
                                      color='rgb(67, 67, 67)'),
                            showarrow=False, align='right'))
    # labeling the first percentage of each bar (x_axis)
    annotations.append(dict(xref='x', yref='y',
                            x=xd[0] / 2, y=yd,
                            text=str(xd[0]) + '%',
                            font=dict(family='Arial', size=11,
                                      color='rgb(248, 248, 255)'),
                            showarrow=False))
    # labeling the first Likert scale (on the top)
    if yd == y_data[-1]:
        annotations.append(dict(xref='x', yref='paper',
                                x=xd[0] / 2, y=1.1,
                                text=top_labels[0],
                                font=dict(family='Arial', size=14,
                                          color='rgb(67, 67, 67)'),
                                showarrow=False))
    space = xd[0]
    for i in range(1, len(xd)):
            # labeling the rest of percentages for each bar (x_axis)
            annotations.append(dict(xref='x', yref='y',
                                    x=space + (xd[i]/2), y=yd,
                                    text=str(xd[i]) + '%',
                                    font=dict(family='Arial', size=11,
                                              color='rgb(248, 248, 255)'),
                                    showarrow=False))
            # labeling the rest Likert scale
            if yd == y_data[-1]:
                annotations.append(dict(xref='x', yref='paper',
                                        x=space + (xd[i]/2), y=1.1,
                                        text=top_labels[i],
                                        font=dict(family='Arial', size=14,
                                                  color='rgb(67, 67, 67)'),
                                        showarrow=False))
            space += xd[i]

fig.update_layout(annotations=annotations)

fig.show()


#South Korea

# func to remove those generic tags in genre
def remove_dup(genre):
    lst = genre.split(', ')
    
    remove_lst = ['International TV Shows','Korean TV Shows','International Movies','Dramas']

    return(','.join(x for x in lst if x not in remove_lst))

df_sk = df[df.country=='South Korea']
df_sk['genre'] = df_sk['listed_in']
df_sk['genre'] = df_sk['genre'].apply(remove_dup)

# further filter genre col
df_sk.loc[df_sk['genre'].str.contains('Action & Adventure'),"genre"] = "Action & Adventure"
df_sk.loc[df_sk['genre'].str.contains('Romantic TV Shows'),"genre"] = "Romantic TV Shows"
df_sk.loc[df_sk['genre'].str.contains('Crime TV Shows'),"genre"] = "Crime TV Shows"
df_sk['genre'] = df_sk['genre'].apply(lambda x:x.split(',')[0])
df_sk.drop(df_sk[df_sk['genre']==''].index,axis=0,inplace=True)

# pie chart
fig = px.pie(df_sk,values=df_sk['genre'].value_counts().values,names=df_sk['genre'].value_counts().index,
             title='Genres in South Korea',color_discrete_sequence= px.colors.qualitative.Bold,height=500)
fig.update_traces(textposition='inside', textinfo='percent+label')

fig.show()


#US

df_us = df[(df.country.str.contains('United States')) & (df.country.notnull())]

df_us['genre'] = df_us['listed_in'].apply(lambda x:x.split(', ')[0])

fig = px.pie(df_us,values=df_us['genre'].value_counts().values,names=df_us['genre'].value_counts().index,
             color_discrete_sequence= px.colors.qualitative.Bold,title='Genres in the US')

fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()


#Release Year

df_year = df[df['release_year']>1999]

fig = px.histogram(df_year,x='release_year',facet_col='type',opacity=0.7,
                   category_orders = {'type':['Movie','TV Show']},
                   color='type',color_discrete_sequence= px.colors.qualitative.Bold,
                  title = "No. of programs by released year")

fig.show()


#Year_added to Netflix

df['year_added'] = df['date_added'].str[-4:]

fig = px.histogram(df,x='year_added',facet_col='type',opacity=0.7,
                   category_orders = {'type':['Movie','TV Show']},
                   color='type',color_discrete_sequence= px.colors.qualitative.Bold,
                  title = "No. of programs by year added to Netflix")

fig.show()


# TV vs Movie across years

df_trend = df.groupby(['year_added','type']).count()['show_id'].reset_index()
df_trend.rename(columns={'show_id':'count'},inplace=True)

df_trend['total'] = df_trend.groupby('year_added')['count'].transform(lambda x:x.sum())
df_trend['percentage'] = df_trend['count']/df_trend['total']
df_trend['percentage'] = pd.Series(["{0:.0f}%".format(x * 100) for x in df_trend['percentage']],index=df_trend.index)

fig = px.bar(df_trend,'year_added','count',color='type',title='Movie vs. TV Show by year added to Netflix',height=500,text='percentage',
            range_x = [2014,2022],color_discrete_sequence= px.colors.qualitative.Bold)
fig.update_xaxes(showgrid=True, ticks="outside")

fig.show()


#TfidfVectorizer

from sklearn.feature_extraction.text import TfidfVectorizer


df1 = df[df['type'] == 'Movie'].reset_index()


tfidf = TfidfVectorizer(analyzer='word',ngram_range=(1,2),min_df=1)
x = tfidf.fit_transform(df1.description)
#print(tfidf.get_feature_names())


from sklearn.metrics.pairwise import sigmoid_kernel
model = sigmoid_kernel(x,x)


def recommendations(title):
    
    indices = pd.Series(data=df1.index,index=df1['title']).drop_duplicates()
    
    idx = indices[title]
    
    sig_scores = list(enumerate(model[idx]))
    
    sig_scores = sorted(sig_scores, key=lambda x: x[1], reverse=True)

    sig_scores = sig_scores[1:11]
    
    idn = [i[0] for i in sig_scores]

    print('Movies you might also like: \n ')
    for i in range(0,10):
        print(i+1, indices.iloc[idn].index[i])
        #print(df1.iloc[idn].description.values[i],'\n')


recommendations('To All the Boys: P.S. I Still Love You')

Movies you might also like: 
 
1 To All the Boys I’ve Loved Before
2 Daagdi Chaawl
3 Petta
4 Petta (Telugu Version)
5 Bhaji In Problem
6 Chris D'Elia: Incorrigible
7 Love You... Love You Not
8 Wildlife
9 The Clapper
10 Lez Bomb


#df1[df1['title'].str.contains('To All the Boys')]

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
0	s1	TV Show	3%	NaN	João Miguel, Bianca Comparato, Michel Gomes, R...	Brazil	August 14, 2020	2020	TV-MA	4 Seasons	International TV Shows, TV Dramas, TV Sci-Fi &...	In a future where the elite inhabit an island ...
1	s2	Movie	7:19	Jorge Michel Grau	Demián Bichir, Héctor Bonilla, Oscar Serrano, ...	Mexico	December 23, 2016	2016	TV-MA	93 min	Dramas, International Movies	After a devastating earthquake hits Mexico Cit...
2	s3	Movie	23:59	Gilbert Chan	Tedd Chan, Stella Chung, Henley Hii, Lawrence ...	Singapore	December 20, 2018	2011	R	78 min	Horror Movies, International Movies	When an army recruit is found dead, his fellow...
3	s4	Movie	9	Shane Acker	Elijah Wood, John C. Reilly, Jennifer Connelly...	United States	November 16, 2017	2009	PG-13	80 min	Action & Adventure, Independent Movies, Sci-Fi...	In a postapocalyptic world, rag-doll robots hi...
4	s5	Movie	21	Robert Luketic	Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...	United States	January 1, 2020	2008	PG-13	123 min	Dramas	A brilliant group of students become card-coun...

TV shows and Movies listed on Netflix¶

1. Data Preprocessing¶

2. EDA¶

3. Movie Recommendation System¶